Known techniques of automatic tuning for adaptive control use a concept known as relay feedback automatic tuning, which has been commercialized for approximately 10 years and which remains attractive owing to its simplicity and robustness. One known and widely accepted approach to regulator automatic tuning uses a process dynamics estimation scheme and a regulator design procedure. In standard relay feedback automatic tuning, as described in U.S. Pat. No. 4,549,123 to Hagglund et al. entitled "Method and an Apparatus in Tuning a PID Regulator" for example, only a single process point, specifically the critical point of the process, is estimated and the parameters of a PID (proportional-integral-derivative) controller are set with respect to this single point. While this method has become standardized and is successful in many process control applications, it suffers from two main problems.
(I) Due to the adoption of the describing function approximation, the estimation of the critical point is not accurate. Under some circumstances (such as oscillatory or significant long dead-time processes, for example) the known method could result in a significant error. For these cases, the tuned regulators which result therefrom may thus yield a poor system performance. Further, as above noted, the standard relay feedback technique estimates only one point on the process frequency response. Such an approach may be insufficient for describing some processes, or for designing model based regulators.
Thus, some modifications to the standard estimation method have been reported. For example, cascading a known linear dynamics to the relay in the standard method can acquire a point other than the critical point. However, the existing relay feedback frequency response estimation methods still suffer from low accuracy, long test time (e.g. when multiple frequency response points are needed) and low efficiency (limited process frequency response information is obtained from one test).
(II) As insufficient and inaccurate frequency response information is utilized in tuning the regulator, the achievable system performance is thus limited. This is particularly true when a process has oscillatory dynamics, or a model-based controller such as the Smith Predictor is considered.